Consumption-Oriented Innovation: A Small Human–AI Case

This paper revisits the idea of consumption-oriented innovation, first presented in 2017 as an attempt to take the direct use of innovation and the agency of consumers more seriously within innovation policy. The earlier framework distinguished consumption that merely responds to innovation from consumption that induces innovation by demanding different characteristics, lower time costs, greater usability, or other functional requirements. Nearly a decade later, an unplanned human–AI dialogue produced a small process case through which the framework can be reconsidered. The dialogue began with a non-specialist question about radioactive half-life and gradually developed into a speculative concept called Atomizer: a deliberately short-lived, relatively high-power radioisotope battery architecture. The scientific and technological pieces subsequently encountered—including P-33, SiC betavoltaic conversion, source–converter integration, and prior patents contemplating P-33 for short-lived applications—were largely pre-existing. The analytically interesting event was therefore not the invention of new nuclear physics. It was the iterative translation of consumption-side dissatisfaction into technical questions, searches, comparisons, rejections, recombinations, and revised requirements. The case suggests that generative AI can reduce the cost of moving between ordinary language of use and specialized bodies of knowledge. This may extend consumption-oriented innovation from ‘letting people consume’ toward ‘letting people specify how they want to consume,’ while preserving an essential distinction from lead-user innovation, producer-led demand articulation, and claims of autonomous AI invention. The paper proposes a recursive model in which imagined use and dissatisfaction repeatedly generate functional requirements, AI-assisted technical search, evaluation of existing solutions, and renewed problem setting. The case is exploratory and does not establish a general theory, but it identifies a tractable research agenda at the intersection of consumption, innovation, and human–AI collaboration.